// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

// clang-format off
#include "main.h"
#include <Eigen/CXX11/Tensor>
// clang-format on

// -------------------------------------------------------------------------- //
// A set of tests for TensorBlockIO: copying data between tensor blocks.

template<int NumDims>
static DSizes<Index, NumDims>
RandomDims(Index min, Index max)
{
	DSizes<Index, NumDims> dims;
	for (int i = 0; i < NumDims; ++i) {
		dims[i] = internal::random<Index>(min, max);
	}
	return DSizes<Index, NumDims>(dims);
}

static internal::TensorBlockShapeType
RandomBlockShape()
{
	return internal::random<bool>() ? internal::TensorBlockShapeType::kUniformAllDims
									: internal::TensorBlockShapeType::kSkewedInnerDims;
}

template<int NumDims>
static size_t
RandomTargetBlockSize(const DSizes<Index, NumDims>& dims)
{
	return internal::random<size_t>(1, dims.TotalSize());
}

template<int Layout, int NumDims>
static Index
GetInputIndex(Index output_index,
			  const array<Index, NumDims>& output_to_input_dim_map,
			  const array<Index, NumDims>& input_strides,
			  const array<Index, NumDims>& output_strides)
{
	int input_index = 0;
	if (Layout == ColMajor) {
		for (int i = NumDims - 1; i > 0; --i) {
			const Index idx = output_index / output_strides[i];
			input_index += idx * input_strides[output_to_input_dim_map[i]];
			output_index -= idx * output_strides[i];
		}
		return input_index + output_index * input_strides[output_to_input_dim_map[0]];
	} else {
		for (int i = 0; i < NumDims - 1; ++i) {
			const Index idx = output_index / output_strides[i];
			input_index += idx * input_strides[output_to_input_dim_map[i]];
			output_index -= idx * output_strides[i];
		}
		return input_index + output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
	}
}

template<typename T, int NumDims, int Layout>
static void
test_block_io_copy_data_from_source_to_target()
{
	using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
	using IODst = typename TensorBlockIO::Dst;
	using IOSrc = typename TensorBlockIO::Src;

	// Generate a random input Tensor.
	DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
	Tensor<T, NumDims, Layout> input(dims);
	input.setRandom();

	// Write data to an output Tensor.
	Tensor<T, NumDims, Layout> output(dims);

	// Construct a tensor block mapper.
	using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
	TensorBlockMapper block_mapper(dims, { RandomBlockShape(), RandomTargetBlockSize(dims), { 0, 0, 0 } });

	// We will copy data from input to output through this buffer.
	Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());

	// Precompute strides for TensorBlockIO::Copy.
	auto input_strides = internal::strides<Layout>(dims);
	auto output_strides = internal::strides<Layout>(dims);

	const T* input_data = input.data();
	T* output_data = output.data();
	T* block_data = block.data();

	for (int i = 0; i < block_mapper.blockCount(); ++i) {
		auto desc = block_mapper.blockDescriptor(i);

		auto blk_dims = desc.dimensions();
		auto blk_strides = internal::strides<Layout>(blk_dims);

		{
			// Read from input into a block buffer.
			IODst dst(blk_dims, blk_strides, block_data, 0);
			IOSrc src(input_strides, input_data, desc.offset());

			TensorBlockIO::Copy(dst, src);
		}

		{
			// Write from block buffer to output.
			IODst dst(blk_dims, output_strides, output_data, desc.offset());
			IOSrc src(blk_strides, block_data, 0);

			TensorBlockIO::Copy(dst, src);
		}
	}

	for (int i = 0; i < dims.TotalSize(); ++i) {
		VERIFY_IS_EQUAL(input_data[i], output_data[i]);
	}
}

template<typename T, int NumDims, int Layout>
static void
test_block_io_copy_using_reordered_dimensions()
{
	// Generate a random input Tensor.
	DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
	Tensor<T, NumDims, Layout> input(dims);
	input.setRandom();

	// Create a random dimension re-ordering/shuffle.
	std::vector<int> shuffle;

	for (int i = 0; i < NumDims; ++i)
		shuffle.push_back(i);
	std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937(g_seed));

	DSizes<Index, NumDims> output_tensor_dims;
	DSizes<Index, NumDims> input_to_output_dim_map;
	DSizes<Index, NumDims> output_to_input_dim_map;
	for (Index i = 0; i < NumDims; ++i) {
		output_tensor_dims[shuffle[i]] = dims[i];
		input_to_output_dim_map[i] = shuffle[i];
		output_to_input_dim_map[shuffle[i]] = i;
	}

	// Write data to an output Tensor.
	Tensor<T, NumDims, Layout> output(output_tensor_dims);

	// Construct a tensor block mapper.
	// NOTE: Tensor block mapper works with shuffled dimensions.
	using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
	TensorBlockMapper block_mapper(output_tensor_dims,
								   { RandomBlockShape(), RandomTargetBlockSize(output_tensor_dims), { 0, 0, 0 } });

	// We will copy data from input to output through this buffer.
	Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());

	// Precompute strides for TensorBlockIO::Copy.
	auto input_strides = internal::strides<Layout>(dims);
	auto output_strides = internal::strides<Layout>(output_tensor_dims);

	const T* input_data = input.data();
	T* output_data = output.data();
	T* block_data = block.data();

	for (Index i = 0; i < block_mapper.blockCount(); ++i) {
		auto desc = block_mapper.blockDescriptor(i);

		const Index first_coeff_index =
			GetInputIndex<Layout, NumDims>(desc.offset(), output_to_input_dim_map, input_strides, output_strides);

		// NOTE: Block dimensions are in the same order as output dimensions.

		using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
		using IODst = typename TensorBlockIO::Dst;
		using IOSrc = typename TensorBlockIO::Src;

		auto blk_dims = desc.dimensions();
		auto blk_strides = internal::strides<Layout>(blk_dims);

		{
			// Read from input into a block buffer.
			IODst dst(blk_dims, blk_strides, block_data, 0);
			IOSrc src(input_strides, input_data, first_coeff_index);

			// TODO(ezhulenev): Remove when fully switched to TensorBlock.
			DSizes<int, NumDims> dim_map;
			for (int j = 0; j < NumDims; ++j)
				dim_map[j] = static_cast<int>(output_to_input_dim_map[j]);
			TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
		}

		{
			// We need to convert block dimensions from output to input order.
			auto dst_dims = blk_dims;
			for (int out_dim = 0; out_dim < NumDims; ++out_dim) {
				dst_dims[output_to_input_dim_map[out_dim]] = blk_dims[out_dim];
			}

			// Write from block buffer to output.
			IODst dst(dst_dims, input_strides, output_data, first_coeff_index);
			IOSrc src(blk_strides, block_data, 0);

			// TODO(ezhulenev): Remove when fully switched to TensorBlock.
			DSizes<int, NumDims> dim_map;
			for (int j = 0; j < NumDims; ++j)
				dim_map[j] = static_cast<int>(input_to_output_dim_map[j]);
			TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
		}
	}

	for (Index i = 0; i < dims.TotalSize(); ++i) {
		VERIFY_IS_EQUAL(input_data[i], output_data[i]);
	}
}

// This is the special case for reading data with reordering, when dimensions
// before/after reordering are the same. Squeezing reads along inner dimensions
// in this case is illegal, because we reorder innermost dimension.
template<int Layout>
static void
test_block_io_copy_using_reordered_dimensions_do_not_squeeze()
{
	DSizes<Index, 3> tensor_dims(7, 9, 7);
	DSizes<Index, 3> block_dims = tensor_dims;

	DSizes<int, 3> block_to_tensor_dim;
	block_to_tensor_dim[0] = 2;
	block_to_tensor_dim[1] = 1;
	block_to_tensor_dim[2] = 0;

	auto tensor_strides = internal::strides<Layout>(tensor_dims);
	auto block_strides = internal::strides<Layout>(block_dims);

	Tensor<float, 3, Layout> block(block_dims);
	Tensor<float, 3, Layout> tensor(tensor_dims);
	tensor.setRandom();

	float* tensor_data = tensor.data();
	float* block_data = block.data();

	using TensorBlockIO = internal::TensorBlockIO<float, Index, 3, Layout>;
	using IODst = typename TensorBlockIO::Dst;
	using IOSrc = typename TensorBlockIO::Src;

	// Read from a tensor into a block.
	IODst dst(block_dims, block_strides, block_data, 0);
	IOSrc src(tensor_strides, tensor_data, 0);

	TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);

	TensorMap<Tensor<float, 3, Layout>> block_tensor(block_data, block_dims);
	TensorMap<Tensor<float, 3, Layout>> tensor_tensor(tensor_data, tensor_dims);

	for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
		for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
			for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
				float block_value = block_tensor(d2, d1, d0);
				float tensor_value = tensor_tensor(d0, d1, d2);
				VERIFY_IS_EQUAL(block_value, tensor_value);
			}
		}
	}
}

// This is the special case for reading data with reordering, when dimensions
// before/after reordering are the same. Squeezing reads in this case is allowed
// because we reorder outer dimensions.
template<int Layout>
static void
test_block_io_copy_using_reordered_dimensions_squeeze()
{
	DSizes<Index, 4> tensor_dims(7, 5, 9, 9);
	DSizes<Index, 4> block_dims = tensor_dims;

	DSizes<int, 4> block_to_tensor_dim;
	block_to_tensor_dim[0] = 0;
	block_to_tensor_dim[1] = 1;
	block_to_tensor_dim[2] = 3;
	block_to_tensor_dim[3] = 2;

	auto tensor_strides = internal::strides<Layout>(tensor_dims);
	auto block_strides = internal::strides<Layout>(block_dims);

	Tensor<float, 4, Layout> block(block_dims);
	Tensor<float, 4, Layout> tensor(tensor_dims);
	tensor.setRandom();

	float* tensor_data = tensor.data();
	float* block_data = block.data();

	using TensorBlockIO = internal::TensorBlockIO<float, Index, 4, Layout>;
	using IODst = typename TensorBlockIO::Dst;
	using IOSrc = typename TensorBlockIO::Src;

	// Read from a tensor into a block.
	IODst dst(block_dims, block_strides, block_data, 0);
	IOSrc src(tensor_strides, tensor_data, 0);

	TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);

	TensorMap<Tensor<float, 4, Layout>> block_tensor(block_data, block_dims);
	TensorMap<Tensor<float, 4, Layout>> tensor_tensor(tensor_data, tensor_dims);

	for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
		for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
			for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
				for (Index d3 = 0; d3 < tensor_dims[3]; ++d3) {
					float block_value = block_tensor(d0, d1, d3, d2);
					float tensor_value = tensor_tensor(d0, d1, d2, d3);
					VERIFY_IS_EQUAL(block_value, tensor_value);
				}
			}
		}
	}
}

template<int Layout>
static void
test_block_io_zero_stride()
{
	DSizes<Index, 5> rnd_dims = RandomDims<5>(1, 30);

	DSizes<Index, 5> input_tensor_dims = rnd_dims;
	input_tensor_dims[0] = 1;
	input_tensor_dims[2] = 1;
	input_tensor_dims[4] = 1;

	Tensor<float, 5, Layout> input(input_tensor_dims);
	input.setRandom();

	DSizes<Index, 5> output_tensor_dims = rnd_dims;

	auto input_tensor_strides = internal::strides<Layout>(input_tensor_dims);
	auto output_tensor_strides = internal::strides<Layout>(output_tensor_dims);

	auto input_tensor_strides_with_zeros = input_tensor_strides;
	input_tensor_strides_with_zeros[0] = 0;
	input_tensor_strides_with_zeros[2] = 0;
	input_tensor_strides_with_zeros[4] = 0;

	Tensor<float, 5, Layout> output(output_tensor_dims);
	output.setRandom();

	using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
	using IODst = typename TensorBlockIO::Dst;
	using IOSrc = typename TensorBlockIO::Src;

	// Write data from input to output with broadcasting in dims [0, 2, 4].
	IODst dst(output_tensor_dims, output_tensor_strides, output.data(), 0);
	IOSrc src(input_tensor_strides_with_zeros, input.data(), 0);
	TensorBlockIO::Copy(dst, src);

	for (int i = 0; i < output_tensor_dims[0]; ++i) {
		for (int j = 0; j < output_tensor_dims[1]; ++j) {
			for (int k = 0; k < output_tensor_dims[2]; ++k) {
				for (int l = 0; l < output_tensor_dims[3]; ++l) {
					for (int m = 0; m < output_tensor_dims[4]; ++m) {
						float input_value = input(0, j, 0, l, 0);
						float output_value = output(i, j, k, l, m);
						VERIFY_IS_EQUAL(input_value, output_value);
					}
				}
			}
		}
	}
}

template<int Layout>
static void
test_block_io_squeeze_ones()
{
	using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
	using IODst = typename TensorBlockIO::Dst;
	using IOSrc = typename TensorBlockIO::Src;

	// Total size > 1.
	{
		DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
		auto strides = internal::strides<Layout>(block_sizes);

		// Create a random input tensor.
		Tensor<float, 5> input(block_sizes);
		input.setRandom();

		Tensor<float, 5> output(block_sizes);

		IODst dst(block_sizes, strides, output.data(), 0);
		IOSrc src(strides, input.data());
		TensorBlockIO::Copy(dst, src);

		for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
			VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
		}
	}

	// Total size == 1.
	{
		DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
		auto strides = internal::strides<Layout>(block_sizes);

		// Create a random input tensor.
		Tensor<float, 5> input(block_sizes);
		input.setRandom();

		Tensor<float, 5> output(block_sizes);

		IODst dst(block_sizes, strides, output.data(), 0);
		IOSrc src(strides, input.data());
		TensorBlockIO::Copy(dst, src);

		for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
			VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
		}
	}
}

#define CALL_SUBTESTS(NAME)                                                                                            \
	CALL_SUBTEST((NAME<float, 1, RowMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 2, RowMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 4, RowMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 5, RowMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 1, ColMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 2, ColMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 4, ColMajor>()));                                                                        \
	CALL_SUBTEST((NAME<float, 5, ColMajor>()));                                                                        \
	CALL_SUBTEST((NAME<bool, 1, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 2, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 4, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 5, RowMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 1, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 2, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 4, ColMajor>()));                                                                         \
	CALL_SUBTEST((NAME<bool, 5, ColMajor>()))

EIGEN_DECLARE_TEST(cxx11_tensor_block_io)
{
	// clang-format off
  CALL_SUBTESTS(test_block_io_copy_data_from_source_to_target);
  CALL_SUBTESTS(test_block_io_copy_using_reordered_dimensions);

  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<RowMajor>());
  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<ColMajor>());

  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<RowMajor>());
  CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<ColMajor>());

  CALL_SUBTEST(test_block_io_zero_stride<RowMajor>());
  CALL_SUBTEST(test_block_io_zero_stride<ColMajor>());

  CALL_SUBTEST(test_block_io_squeeze_ones<RowMajor>());
  CALL_SUBTEST(test_block_io_squeeze_ones<ColMajor>());
	// clang-format on
}
